Fe3N Nanoparticle-Encapsulated N-Doped Carbon Nanotubes on Biomass-Derived Carbon Cloth as Self-Standing Electrocatalyst for Oxygen Reduction Reaction.

The design and fabrication of low-cost catalysts for highly efficient oxygen reduction are of paramount importance for various renewable energy-related technologies, such as fuel cells and metal-air batteries. Herein, we report the synthesis of Fe3N nanoparticle-encapsulated N-doped carbon nanotubes on the surface of a flexible biomass-derived carbon cloth (Fe3N@CNTs/CC) via a simple one-step carbonization process. Taking advantage of its unique structure, Fe3N@CNTs/CC was employed as a self-standing electrocatalyst for oxygen reduction reaction (ORR) and possessed high activity as well as excellent long-term stability and methanol resistance in alkaline media. Remarkably, Fe3N@CNT/CC can directly play the role of both a gas diffusion layer and an electrocatalytic cathode in a zinc-air battery without additional means of catalyst loading, and it displays higher open-circuit voltage, power density, and specific capacity in comparison with a commercial Pt/C catalyst. This work is anticipated to inspire the design of cost-effective, easily prepared, and high-performance air electrodes for advanced electrochemical applications.

Correlation of SIDT1 with Poor Prognosis and Immune Infiltration in Patients with Non-Small Cell Lung Cancer.

PURPOSE: To investigate the expression profile of systemic RNA interference deficient-1 transmembrane family member 1 (SIDT1) in patients with non-small cell lung cancer (NSCLC) and assess its prognostic value and immune infiltration. MATERIALS AND METHODS: A tissue microarray (TMA) was purchased containing 60 paired NSCLC tissues and matched para-tumor tissues, and another TMA was constructed comprising 140 NSCLC tissues. Then, immunohistochemical staining and scoring were performed. Finally, we evaluated the expression profiles and prognostic values of SIDT1 in NSCLC. Correlation between SIDT1 gene expression and the immune microenvironment of NSCLC was investigated using the ESTIMATE and CIBERSORT algorithms. RESULTS: SIDT1 was mainly detected in the cytoplasm and the cell membrane. SIDT1 expression was significantly higher in tumor tissues than in para-tumor tissues. High expression of SIDT1 was correlated with the NSCLC stage. High SIDT1 expression signals worsening overall survival (OS) in NSCLC patients, especially in stage I. In LUSC, high SIDT1 expression is an independent prognostic factor for worsening OS. CONCLUSION: High SIDT1 expression predicted a low survival rate in LUSC patients, suggesting it may be a potential target for prognostic assessment of NSCLC patients.

High KIF11 expression is associated with poor outcome of NSCLC.

PURPOSE: To clarify the correlation between KIF11 (kinesin family member 11) and clinicopathologic characteristics of non-small cell lung cancer (NSCLC) and identify the prognostic value of KIF11 in patients with NSCLC. METHODS: For investigating the expression of KIF11 in NSCLC, two tissue microarrays (TMAs: one contained 60 paired NSCLC tissues and paratumor tissues, the other contained 140 NSCLC tissues and 10 normal lung tissues) were constructed, stained, and scored. The Cancer Genome Atlas (TCGA) datasets were used to explore the differential expression level of KIF11 between NSCLC and paratumor. Kaplan-Meier survival curves were plotted and multivariate analysis were carried out. RESULTS: The staining of KIF11 mainly distributed throughout the cytoplasm of tumor cells. Its expression was higher in NSCLC than paratumor cells, and similar results were obtained from TCGA datasets. We found that high expression of KIF11 had a significant correlation with lymph node metastases (p = 0.024) and pathologic stage (p = 0.018); that significant difference was not found in any other clinicopathologic characteristic. As univariate and multivariate analysis showed, KIF11 expression was significantly correlated with overall survival time of NSCLC (p = 0.002, p = 0.025, respectively). High KIF11 expression was found to significantly associate with overall survival of stage II-III (p = 0.001) and lung adenocarcinoma (p = 0.036). CONCLUSION: High KIF11 expression predicts poor outcome in NSCLC. KIF11 is expected to be a viable prognostic biomarker for NSCLC.

Bond Behavior of Reinforced Concrete Considering Freeze-Thaw Cycles and Corrosion of Stirrups.

In relatively cold environments, the combination of freeze-thaw and steel bar corrosion is a key factor affecting the durability of concrete. The adjustment of the stirrup ratio would change the mechanical performance of surrounding concrete, while the circumferential compressive stress can further improve the bonding performance. Hence, based on eccentrically tensioned specimens, the influence of corrosion of stirrups and freeze-thaw of concrete on bond properties is discussed in this paper. The monotonic pull-out test of reinforced concrete specimens is carried out to study the variation rules of bond strength and slip between steel bar and concrete under the coupling action of corrosion rate, freeze-thaw times and stirrup spacing. Based on the experimental data, the empirical formula for the ultimate bond strength is obtained, and a bond-slip constitutive model is established considering the stirrup spacing, stirrup corrosion rate and freeze-thaw times. Then, a refined finite element pull-out specimen model is established by ABAQUS simulation, and the numerical simulation results are compared with the real test ones, so as to make up for the deficiencies in the test and lay the foundation for further finite element analysis.

[Efficient and Stable Operation of Shortcut Nitrification by Entrapping Ammonia Oxidizing Bacteria].

In order to improve the efficiency of shortcut nitrification by entrapping ammonia oxidizing bacteria, the technique of ammonia oxidizing bacteria(AOB) enrichment culture was studied. The continuous operation method was used to inhibit the growth of nitrite-oxidizing bacteria(NOB) by free ammonia, at the same time, the NOB was gradually washed out of the system through the technology of sludge discharge. Polyvinyl alcohol(PVA) was used as the embedding material to immobilize the ammonia oxidizing bacteria after enrichment culture. The embedding rate of the reactor was 8%. The ammonia oxidation rate was increased by improving ammonia nitrogen loading in the continuous operation mode. The results showed that the ammonia oxidation rate(NH4+-N/VSS)was as high as 2.028 g·(g·d)-1 and the nitrite nitrogen accumulation rate was stabilized at more than 90%. High-throughput sequencing analysis was conducted on the bacterial community composition before and after enrichment culture, and the experimental result indicated that the culture diversity of raw sludge was larger. Nitrosomonas and Nitrospirae, which have nitrification function, accounted for 0.24% and 2.7%, respectively. The diversity of the activated sludge decreased significantly after the enrichment culture, Nitrosomonas(18%) became the dominant bacteria whereas Nitrospira occupied only 0.02%. After he embedding of ammonia oxidizing bacteria, the shortcut nitrification was realized rapidly. Finally, the shortcut nitrification rate reached 50 mg·(L·h)-1and the nitrite nitrogen accumulation was above 90%.

Comparison of FDA Approved Kinase Targets to Clinical Trial Ones: Insights from Their System Profiles and Drug-Target Interaction Networks.

Kinase is one of the most productive classes of established targets, but the majority of approved drugs against kinase were developed only for cancer. Intensive efforts were therefore exerted for releasing its therapeutic potential by discovering new therapeutic area. Kinases in clinical trial could provide great opportunities for treating various diseases. However, no systematic comparison between system profiles of established targets and those of clinical trial ones was conducted. The reveal of probable difference or shift of trend would help to identify key factors defining druggability of established targets. In this study, a comparative analysis of system profiles of both types of targets was conducted. Consequently, the systems profiles of the majority of clinical trial kinases were identified to be very similar to those of established ones, but percentages of established targets obeying the system profiles appeared to be slightly but consistently higher than those of clinical trial targets. Moreover, a shift of trend in the system profiles from the clinical trial to the established targets was identified, and popular kinase targets were discovered. In sum, this comparative study may help to facilitate the identification of the druggability of established drug targets by their system profiles and drug-target interaction networks.

Empirical Correction to the Likelihood Ratio Statistic for Structural Equation Modeling with Many Variables.

Survey data typically contain many variables. Structural equation modeling (SEM) is commonly used in analyzing such data. The most widely used statistic for evaluating the adequacy of a SEM model is T ML, a slight modification to the likelihood ratio statistic. Under normality assumption, T ML approximately follows a chi-square distribution when the number of observations (N) is large and the number of items or variables (p) is small. However, in practice, p can be rather large while N is always limited due to not having enough participants. Even with a relatively large N, empirical results show that T ML rejects the correct model too often when p is not too small. Various corrections to T ML have been proposed, but they are mostly heuristic. Following the principle of the Bartlett correction, this paper proposes an empirical approach to correct T ML so that the mean of the resulting statistic approximately equals the degrees of freedom of the nominal chi-square distribution. Results show that empirically corrected statistics follow the nominal chi-square distribution much more closely than previously proposed corrections to T ML, and they control type I errors reasonably well whenever N ≥ max(50,2p). The formulations of the empirically corrected statistics are further used to predict type I errors of T ML as reported in the literature, and they perform well.